The malaria parasite, Plasmodium falciparum, continues to kill people at an alarming rate. Solving the difficult challenge of emerging resistance to artemisinin (ART)-based combination therapies will take the full set of traditional and innovative new tools. An exciting recent discovery pinpointed mutations in a chromosome 13 gene that encodes a kelch propeller domain as a marker for emerging ART-resistance (ART-R), however nothing is known about its function or its interacting partners. An important step in converting primary knowledge of genome-scale information to understanding the mechanisms of ART-R is the elucidation of the interactions among the component parts of the biological system. Knowledge of these components can guide appropriate interventions to prolong this crucial drug. We outline a novel approach that is robust to 2 main limitations of genome-wide data: false positives and incomplete information. We validate our approach with the gold standard knowledge that pfcrt is the primary determinant of chloroquine (CQ) resistance and demonstrate its utility for dissecting ART-R. After constructing a comprehensive reference transcriptional network, we generated 2 novel and independent query lists: i) genes that are 'rewired', i.e. diverge dramatically in their co- expression relationships, between CQR and CQS parasites and ii) genes interrupted by piggyBac (pB) insertions that exhibit altered drug susceptibility. For each of these richly informative input gene sets, we used the reference network to find nodes that complete a short path connection among many of these genes. The immediate goal of this application is to apply this approach to the urgent and vexing problem of emerging ART- R, by expanding the reference network and determining the top rewired genes using an extraordinary panel of clinical isolates from the Thailand-Myanmar border (Aim 1) and by screening 128 pB mutants and transfected lines for shifts in ART susceptibility and transcriptional response profiles (Aim 2). This information will be used to independently validate candidate genes and predict functional interactions (Aim 3).